Research map / Learning theory and algorithms / Combinatorial and search optimization
Bounding Quality in Diverse Planning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the Sym top-quality planner to generate candidates and optimize bounded diversity with the proposed subset-selection approach | bQbD-k · IPC 1998-2018 STRIPS benchmarks, k=5, qm=1.00 | overall coverage: 631 vs 453 | Helped |
| Use the FI top-quality planner to generate candidates and optimize bounded diversity with the proposed subset-selection approach | bQbD-k · IPC 1998-2018 STRIPS benchmarks, k=5, qm=1.00 | overall coverage: 594 vs 453 | Helped |
| Use the Sym top-quality planner to generate candidates and optimize bounded diversity with the proposed subset-selection approach | bQbD-k · IPC 1998-2018 STRIPS benchmarks, k=1000, qm=1.00 | overall coverage: 435 vs 238 | Helped |
| Use greedy subset selection instead of optimal mixed-integer subset selection | bQ-k · Test tasks, k=5, qm=1.00 | diversity score: 80 out of 121 | No clear effect |
| Use greedy subset selection instead of optimal mixed-integer subset selection | bQ-k · Test tasks, k=5, qm=1.00 | runtime: under 1.2 seconds; up to 500 seconds | Helped |
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